Merge pull request #8643 from JiayiFeng/remove_evaluator

Removes Accuracy

Author: JiayiFeng

benchmark/cluster/vgg16/vgg16_fluid.py

@@ -1,11 +1,11 @@
 #   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
-# 
+#
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
-# 
+#
 #     http://www.apache.org/licenses/LICENSE-2.0
-# 
+#
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -138,13 +138,14 @@ def main():
     avg_cost = fluid.layers.mean(x=cost)
 
     # Evaluator
-    accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
+    batch_size = fluid.layers.create_tensor(dtype='int64')
+    batch_acc = fluid.layers.accuracy(
+        input=predict, label=label, total=batch_size)
 
     # inference program
     inference_program = fluid.default_main_program().clone()
     with fluid.program_guard(inference_program):
-        test_target = accuracy.metrics + accuracy.states
-        inference_program = fluid.io.get_inference_program(test_target)
+        inference_program = fluid.io.get_inference_program(batch_acc)
 
     # Optimization
     optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
@@ -157,27 +158,30 @@ def main():
 
     # test
     def test(exe):
-        accuracy.reset(exe)
+        test_pass_acc = fluid.average.WeightedAverage()
         for batch_id, data in enumerate(test_reader()):
             img_data = np.array(map(lambda x: x[0].reshape(data_shape),
                                     data)).astype("float32")
             y_data = np.array(map(lambda x: x[1], data)).astype("int64")
             y_data = y_data.reshape([-1, 1])
 
-            exe.run(inference_program,
-                    feed={"pixel": img_data,
-                          "label": y_data})
+            outs = exe.run(inference_program,
+                           feed={"pixel": img_data,
+                                 "label": y_data},
+                           fetch_list=[batch_acc, batch_size])
+            test_pass_acc.add(value=np.array(outs[0]), weight=np.array(outs[1]))
 
-        return accuracy.eval(exe)
+        return test_pass_acc.eval()
 
     def train_loop(exe, trainer_prog):
         iters = 0
         ts = time.time()
+        train_pass_acc = fluid.average.WeightedAverage()
         for pass_id in range(args.num_passes):
             # train
             start_time = time.time()
             num_samples = 0
-            accuracy.reset(exe)
+            train_pass_acc.reset()
             with profiler.profiler("CPU", 'total') as prof:
                 for batch_id, data in enumerate(train_reader()):
                     ts = time.time()
@@ -187,21 +191,22 @@ def train_loop(exe, trainer_prog):
                     y_data = np.array(map(lambda x: x[1], data)).astype("int64")
                     y_data = y_data.reshape([-1, 1])
 
-                    loss, acc = exe.run(
+                    loss, acc, b_size = exe.run(
                         trainer_prog,
                         feed={"pixel": img_data,
                               "label": y_data},
-                        fetch_list=[avg_cost] + accuracy.metrics)
+                        fetch_list=[avg_cost, batch_acc, batch_size])
                     iters += 1
                     num_samples += len(data)
+                    train_pass_acc.add(value=acc, weight=b_size)
                     print(
                         "Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, Speed = %.2f img/s"
                         % (pass_id, iters, loss, acc,
                            len(data) / (time.time() - ts))
                     )  # The accuracy is the accumulation of batches, but not the current batch.
 
             pass_elapsed = time.time() - start_time
-            pass_train_acc = accuracy.eval(exe)
+            pass_train_acc = train_pass_acc.eval()
             pass_test_acc = test(exe)
             print(
                 "Pass = %d, Training performance = %f imgs/s, Train accuracy = %f, Test accuracy = %f\n"

python/paddle/fluid/__init__.py

@@ -28,6 +28,7 @@
 import optimizer
 import backward
 import regularizer
+import average
 from param_attr import ParamAttr, WeightNormParamAttr
 from data_feeder import DataFeeder
 from core import LoDTensor, CPUPlace, CUDAPlace

python/paddle/fluid/average.py

@@ -0,0 +1,61 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+"""
+    Class of all kinds of Average.
+
+    All Averages are accomplished via Python totally. 
+    They do not change Paddle's Program, nor do anything to
+    modify NN model's configuration. They are completely 
+    wrappers of Python functions.
+"""
+
+
+def _is_number_(var):
+    return isinstance(var, int) or isinstance(var, float) or (isinstance(
+        var, np.ndarray) and var.shape == (1, ))
+
+
+def _is_number_or_matrix_(var):
+    return _is_number_(var) or isinstance(var, np.ndarray)
+
+
+class WeightedAverage(object):
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.numerator = None
+        self.denominator = None
+
+    def add(self, value, weight):
+        if not _is_number_or_matrix_(value):
+            raise ValueError(
+                "The 'value' must be a number(int, float) or a numpy ndarray.")
+        if not _is_number_(weight):
+            raise ValueError("The 'weight' must be a number(int, float).")
+
+        if self.numerator is None or self.denominator is None:
+            self.numerator = value * weight
+            self.denominator = weight
+        else:
+            self.numerator += value * weight
+            self.denominator += weight
+
+    def eval(self):
+        if self.numerator is None or self.denominator is None:
+            raise ValueError(
+                "There is no data to be averaged in WeightedAverage.")
+        return self.numerator / self.denominator

python/paddle/fluid/evaluator.py

@@ -108,44 +108,6 @@ def create_state(self, suffix, dtype, shape):
         return state
 
 
-class Accuracy(Evaluator):
-    """
-    Average Accuracy for multiple mini-batches.
-    """
-
-    def __init__(self, input, label, k=1, **kwargs):
-        super(Accuracy, self).__init__("accuracy", **kwargs)
-        main_program = self.helper.main_program
-        if main_program.current_block().idx != 0:
-            raise ValueError("You can only invoke Evaluator in root block")
-
-        self.total = self.create_state(dtype='int64', shape=[1], suffix='total')
-        self.correct = self.create_state(
-            dtype='int64', shape=[1], suffix='correct')
-        total = self.helper.create_tmp_variable(dtype='int')
-        correct = self.helper.create_tmp_variable(dtype='int')
-        acc = layers.accuracy(
-            input=input, label=label, k=k, total=total, correct=correct)
-        total = layers.cast(x=total, dtype='int64')
-        correct = layers.cast(x=correct, dtype='int64')
-        layers.sums(input=[self.total, total], out=self.total)
-        layers.sums(input=[self.correct, correct], out=self.correct)
-
-        self.metrics.append(acc)
-
-    def eval(self, executor, eval_program=None):
-        if eval_program is None:
-            eval_program = Program()
-        block = eval_program.current_block()
-        with program_guard(main_program=eval_program):
-            total = _clone_var_(block, self.total)
-            correct = _clone_var_(block, self.correct)
-            total = layers.cast(total, dtype='float32')
-            correct = layers.cast(correct, dtype='float32')
-            out = layers.elementwise_div(x=correct, y=total)
-        return np.array(executor.run(eval_program, fetch_list=[out])[0])
-
-
 class ChunkEvaluator(Evaluator):
     """
     Accumulate counter numbers output by chunk_eval from mini-batches and

python/paddle/fluid/layers/__init__.py

@@ -28,6 +28,8 @@
 from math_op_patch import *
 import detection
 from detection import *
+import metric
+from metric import *
 from learning_rate_scheduler import *
 
 __all__ = []
@@ -39,4 +41,5 @@
 __all__ += ops.__all__
 __all__ += device.__all__
 __all__ += detection.__all__
+__all__ += metric.__all__
 __all__ += learning_rate_scheduler.__all__

python/paddle/fluid/layers/metric.py

@@ -0,0 +1,57 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+All layers just related to metric.
+"""
+
+from ..layer_helper import LayerHelper
+from ..initializer import Normal, Constant
+from ..framework import Variable
+from ..param_attr import ParamAttr
+
+__all__ = ['accuracy']
+
+
+def accuracy(input, label, k=1, correct=None, total=None):
+    """
+    This function computes the accuracy using the input and label.
+    The output is the top_k inputs and their indices.
+    """
+    helper = LayerHelper("accuracy", **locals())
+    topk_out = helper.create_tmp_variable(dtype=input.dtype)
+    topk_indices = helper.create_tmp_variable(dtype="int64")
+    helper.append_op(
+        type="top_k",
+        inputs={"X": [input]},
+        outputs={"Out": [topk_out],
+                 "Indices": [topk_indices]},
+        attrs={"k": k})
+    acc_out = helper.create_tmp_variable(dtype="float32")
+    if correct is None:
+        correct = helper.create_tmp_variable(dtype="int64")
+    if total is None:
+        total = helper.create_tmp_variable(dtype="int64")
+    helper.append_op(
+        type="accuracy",
+        inputs={
+            "Out": [topk_out],
+            "Indices": [topk_indices],
+            "Label": [label]
+        },
+        outputs={
+            "Accuracy": [acc_out],
+            "Correct": [correct],
+            "Total": [total],
+        })
+    return acc_out

python/paddle/fluid/layers/nn.py

@@ -35,7 +35,6 @@
     'cos_sim',
     'cross_entropy',
     'square_error_cost',
-    'accuracy',
     'chunk_eval',
     'sequence_conv',
     'conv2d',
@@ -1022,40 +1021,6 @@ def square_error_cost(input, label):
     return square_out
 
 
-def accuracy(input, label, k=1, correct=None, total=None):
-    """
-    This function computes the accuracy using the input and label.
-    The output is the top_k inputs and their indices.
-    """
-    helper = LayerHelper("accuracy", **locals())
-    topk_out = helper.create_tmp_variable(dtype=input.dtype)
-    topk_indices = helper.create_tmp_variable(dtype="int64")
-    helper.append_op(
-        type="top_k",
-        inputs={"X": [input]},
-        outputs={"Out": [topk_out],
-                 "Indices": [topk_indices]},
-        attrs={"k": k})
-    acc_out = helper.create_tmp_variable(dtype="float32")
-    if correct is None:
-        correct = helper.create_tmp_variable(dtype="int64")
-    if total is None:
-        total = helper.create_tmp_variable(dtype="int64")
-    helper.append_op(
-        type="accuracy",
-        inputs={
-            "Out": [topk_out],
-            "Indices": [topk_indices],
-            "Label": [label]
-        },
-        outputs={
-            "Accuracy": [acc_out],
-            "Correct": [correct],
-            "Total": [total],
-        })
-    return acc_out
-
-
 def chunk_eval(input,
                label,
                chunk_scheme,
@@ -3182,7 +3147,7 @@ def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
             data = fluid.layers.data(name='data', shape=[128], dtype='float32')
             label = fluid.layers.data(name='label', shape=[100], dtype='int64')
             fc = fluid.layers.fc(input=data, size=100)
-            out = fluid.layers.smooth_l1(logits=fc, label=label)
+            out = fluid.layers.smooth_l1(x=fc, y=label)
     """
     helper = LayerHelper('smooth_l1_loss', **locals())
     diff = helper.create_tmp_variable(dtype=x.dtype)

python/paddle/fluid/tests/book_memory_optimization/test_memopt_image_classification_train.py

@@ -122,7 +122,8 @@ def conv_block(input, num_filter, groups, dropouts):
 optimizer = fluid.optimizer.Adam(learning_rate=0.001)
 opts = optimizer.minimize(avg_cost)
 
-accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
+batch_size = fluid.layers.create_tensor(dtype='int64')
+batch_acc = fluid.layers.accuracy(input=predict, label=label, total=batch_size)
 
 fluid.memory_optimize(fluid.default_main_program())
 
@@ -144,13 +145,17 @@ def conv_block(input, num_filter, groups, dropouts):
 exe.run(fluid.default_startup_program())
 
 i = 0
+
+accuracy = fluid.average.WeightedAverage()
 for pass_id in range(PASS_NUM):
-    accuracy.reset(exe)
+    accuracy.reset()
     for data in train_reader():
-        loss, acc = exe.run(fluid.default_main_program(),
-                            feed=feeder.feed(data),
-                            fetch_list=[avg_cost] + accuracy.metrics)
-        pass_acc = accuracy.eval(exe)
+        loss, acc, weight = exe.run(
+            fluid.default_main_program(),
+            feed=feeder.feed(data),
+            fetch_list=[avg_cost, batch_acc, batch_size])
+        accuracy.add(value=acc, weight=weight)
+        pass_acc = accuracy.eval()
         print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
             pass_acc))
         # this model is slow, so if we can train two mini batch, we think it works properly.